AdaBoost-driven multi-parameter real-time warning of rock burst risk in coal mines

Abstract

The stope dynamic disaster, which occurs around the mining space and is represented by stress-type rock burst and fracture-type rock burst, seriously affects the safety production of coal mine. How to effectively forewarn rock burst risk to reduce the disaster caused by rock burst is an urgent problem to be solved in stope. In this paper, the spatiotemporal parameters that affect the occurrence of stope dynamic disaster are collected, and the big data of stope state is established. Due to the complex temporal and spatial parameters of rock bursts with different intensities occurrence and their varying degrees, it is difficult for some machine learning methods to excavate their internal relationships and make accurate warning. In this paper, (1) a big data platform is used to record and integrate the multiple parameters of stope dynamic disaster, and data preprocessing technology is used to de-noise and standardize them. (2) Based on the fused historical data, a strong classifier is iteratively found by the classification algorithm AdaBoost to identify the existence of the rock burst risk, so as to achieve the purpose of accurately and timely warning of rock burst risk. The research of this paper uses big data mining technology and machine learning method to carry out intelligent perception warning of the rock burst risk in real time. The experimental results show that the proposed method has a good effect and is of great significance to the prevention and control of rock burst disaster in stope.